In recent years, Deep Reinforcement Learning (DRL) has emerged as a promising
method for robot collision avoidance. However, such DRL models often come with
limitations, such as adapting effectively to structured environments containing
various pedestrians. In order to solve this difficulty, previous research has
attempted a few approaches, including training an end-to-end solution by
integrating a waypoint planner with DRL and developing a multimodal solution to
mitigate the drawbacks of the DRL model. However, these approaches have
encountered several issues, including slow training times, scalability
challenges, and poor coordination among different models. To address these
challenges, this paper introduces a novel approach called evolutionary
curriculum training to tackle these challenges. The primary goal of
evolutionary curriculum training is to evaluate the collision avoidance model's
competency in various scenarios and create curricula to enhance its
insufficient skills. The paper introduces an innovative evaluation technique to
assess the DRL model's performance in navigating structured maps and avoiding
dynamic obstacles. Additionally, an evolutionary training environment generates
all the curriculum to improve the DRL model's inadequate skills tested in the
previous evaluation. We benchmark the performance of our model across five
structured environments to validate the hypothesis that this evolutionary
training environment leads to a higher success rate and a lower average number
of collisions. Further details and results at our project website.

本文介绍了一种名为进化课程训练的新方法，以应对机器人避碰中经常出现的一些挑战，包括在结构化环境中适应多种行人，本方法能够提高深度强化学习模型在此类复杂场景中的成功率和降低平均碰撞率。

基于演化课程训练的 DRL 导航系统训练

Evolutionary Curriculum Training for DRL-Based Navigation Systems

In this work, we present a multimodal solution to the problem of 4D face
reconstruction from monocular videos. 3D face reconstruction from 2D images is
an under-constrained problem due to the ambiguity of depth. State-of-the-art
methods try to solve this problem by leveraging visual information from a
single image or video, whereas 3D mesh animation approaches rely more on audio.
However, in most cases (e.g. AR/VR applications), videos include both visual
and speech information. We propose AVFace that incorporates both modalities and
accurately reconstructs the 4D facial and lip motion of any speaker, without
requiring any 3D ground truth for training. A coarse stage estimates the
per-frame parameters of a 3D morphable model, followed by a lip refinement, and
then a fine stage recovers facial geometric details. Due to the temporal audio
and video information captured by transformer-based modules, our method is
robust in cases when either modality is insufficient (e.g. face occlusions).
Extensive qualitative and quantitative evaluation demonstrates the superiority
of our method over the current state-of-the-art.

该研究提出了一种基于多模态的方法来实现从单目视频中进行 4D 人脸重建，并使用 AVFace 技术准确地重构任何人的面部和唇部运动，而无需任何 3D 地面真实测试。